In practice, many nonlinear reconstruction techniques are available including neural networks [1]}, Gaussian process regression [2]}, and recurrent neural networks for time-delayed measurements [3]}.
Using Gaussian process regression and the two sensor locations marked by green stars in Figure REF , we obtain near-perfect, robust reconstruction of the leading 100 principal components.
The resulting reconstruction accuracy for the flow-fields on a held-out set of 250 snapshots is \(R^2=0.986\) .
